Voice control devices have many applications including logistics warehouse control and intelligent home design. In the electronic industry, it is also popular to add voice control functionality to products such as home appliances and toys. There are a number of voice recognition systems in the market and very mature products in both hardware and software are available. They are usually based on a hidden Markov chain and are trained to recognize the commands using a large database of speech signals. A system can be programmed to take speech commands to activate other functions. However, in a noisy work environment, various background noises create an application constraint to the system. A certain signal-to-noise ratio is required for such a system to work properly. When the signal-to-noise ratio is too low, the performance of such a system will deteriorate significantly. In an acoustic environment with possible strong near-field noise, a microphone array is required to suppress noise while leaving the distortion of the speech to a minimum. Since this problem is very difficult to be described by a priori models, sequences of calibration signals are often used for the design of the beamformer.
Generally, the optimal beamformer design problem is a multi-criteria decision problem, where the criteria are the level of distortion and the level of noise suppression. The least-squares technique (LS) and the signal-to-noise ratio (SNR) are often used to optimize for the performance of the beamformer. However, the least-squares technique tends to concentrate on distortion control with deficiency in noise suppression. Similarly, using the signal-to-noise ratio, distortion is usually significant, although noise suppression can be achieved. For voice control applications, a balance is required between the two extreme controls. One way to improve performance is to increase the length of the filter. Nevertheless, it is a very costly way and it still cannot guarantee an acceptable design for voice control devices.